Metrics 0.3.0 daviesbouldin

Lodestar.Metrics 0.3.0. This page is frozen at that release. Read the current documentation for what main says now. A link to a decision or a migration page follows main, and leaves the archive.

DaviesBouldin

For each cluster, the worst ratio of "how spread these two are" to "how far apart they sit", averaged over the clusters. Lower is better — the opposite direction to CalinskiHarabasz and Silhouette, and 0 is the floor.

That inversion is the one thing worth checking before reading a table of these: a clustering that improves moves this number down and the other two up, and a reader who takes the three as interchangeable will read one of them backwards.

Like CalinskiHarabasz, it scores a clustering against nothing but the samples, takes a feature matrix rather than two label vectors, and has no precomputed-distance form — both read cluster centroids, which a distance matrix does not carry.

Two clusters sharing a centroid contribute nothing

The ratio would divide by zero. The reference replaces a zero centroid distance with infinity before dividing, so the pair scores 0 and drops out of its cluster's worst case. Measured, four identical points split into two clusters score 0 — as do two well-separated points each duplicated into a cluster of its own, which is a perfect clustering. The floor is reached from both directions, and the number cannot tell them apart.

Members

Member	What it does
DaviesBouldin.Score	The mean worst-case similarity between a cluster and any other.